Markers derived from continuous glucose monitoring data to guide exercise for T1DM

This project aims to advance diabetes care by leveraging continuous glucose monitoring (CGM) data and machine learning methodologies, with a focus on type 1 diabetes (T1D). The project builds upon the Type 1 Diabetes and Exercise Initiative (T1DEXI) study to better understand how various exercise types and timings affect glucose response in individuals with T1D.
Aim 1: Perform data mining on the T1DEXI dataset and explore the effects of different exercise sessions on T1D.
Aim 2: Develop a predictive machine learning model for optimal exercise outcomes
Aim 3: Explore the missing value issues in the CGM data from the T1DEXI study and develop methods and strategies to predict missing values in CGM readings.